A pay-for-placement search engine works like an online version of the Yellow Pages: a user performs a search, and the system displays paid advertiser listings that match the user's query. Because screen real estate is limited, a user will typically only see a small fraction of the listings that match his query. So in order to create a workable system, the search engine provider needs to decide how often to display each listing, how much to charge, and the order in which listings should appear.
The current state of the art is Overture's search engine, available at www.overture.com. Overture uses a scheme known as bid-for-rank, in which it charges advertisers by the click and orders listings based on how much each advertiser is willing to pay for each click. An advertiser can bid whatever he likes, with a minimum bid of five cents per click. Many web sites display Overture search results, and since each shows a different number of search results starting at the top of the list, there is strong incentive to be near the top. The advertiser with the highest bid always appears, and advertisers appear less and less frequently as their bids decrease. In theory, the advertisers that provide the highest quality of service bid the most and appear at the top of the list. In practice, the system rarely works this well. The scheme is conceptually simple, but it has a number of problems that make it frustrating for both users and advertisers.
From a user's perspective, the problem is that bid-for-rank can lead to irrelevant, unwanted search listings close to the top. Advertisers have complete control over the order in which their listings appear, and smart advertisers can take advantage of this freedom to get free exposure at the user's expense. As an example, imagine there is an advertiser selling pet ferrets. The advertiser appears under very specific search terms, like “ferret” and “pet ferret”, and also under more generic terms, like “pets”. When a user searches for “pet ferret”, it makes sense that the ferret advertiser appears at the top of the list, because the user is almost certainly looking for what he is selling. For a more generic term like “pets”, though, the ferret advertiser should not appear near the top. A user that types in “pets” is much more likely to be looking for a dog or a cat than a ferret.
Unfortunately, if the ferret advertiser is smart he will create a listing that reads something like, “Great pet FERRETS! Super CHEAP!” He can afford to bid a very high price for this listing—a price that takes him to the top of the list—because he knows that when a user clicks on it, the user clearly sees it is about ferrets. There is enough information in the listing that the advertiser is very likely to get a sale. In the bid-for-rank system, the advertiser does not pay for being at the top of the list; he only pays if a user actually clicks on his listing. This listing is so specific that a user will only click on it if he is interested in ferrets, so there is very little risk to the advertiser, even if the listing appears under a generic term like “pets”. The consequence is that the first search result for “pets” is a listing that is only relevant to a few users. For everyone else the listing is useless, and the overall search experience is poor. The ferret phenomenon is common with pay-for-placement search engines. The Yellow Pages does not suffer from this problem, because advertisers are forced to pay based on how much space they take up on the page.
From an advertiser's perspective, the problem with bid-for-rank is that it is complicated, and hard for him to know what he gets. When an advertiser bids to a particular rank, he has no way to calculate in advance how many clicks he is likely to receive, or how much money he is likely to spend, or whether he would have a higher profit at some other rank. He cannot even be sure that he will get the rank that he wants, because another advertiser might step in after him with a higher bid. If the advertiser is on a fixed budget, he must continually monitor his spending to make sure he does not go over budget, while still keeping his bids high enough to get the maximum possible number of clicks. Typically, he must deal with all of these uncertainties for fifty or a hundred different search terms, each of which has its own bids.
As an example, suppose an advertiser is currently bidding $1.00 to be in the number 2 position for the search term “fresh fish”. He can stay where he is, increase his bid to $1.20 to move up to rank 1, or decrease his bid to $0.80 to move down to rank 3. In order to make this decision, he needs to know how many clicks his is likely to receive at each of the three ranks. It is next to impossible for him to get this information. Given the current state of the art, in fact, search engine providers cannot even say for certain that he will get more clicks at rank 1 than at rank 3. Even if the advertiser decides it does make sense to bid up to rank 1, there is no guarantee that a few hours later one of the other advertisers will not outbid him. Or, equally possible, the advertiser can bid up to rank 1, and then discover a few days later that he is overpaying because the advertisers below him have dropped out. The advertiser must continually monitor his bid and position to make sure he gets what he wants, without overpaying. The current state of the art is to keep track of bids using electronic bidding agents. Examples are at www.gotoast.com, www.did-it.com and www.pay-per-click-bid-managers.com. However, these are limited in how well they can perform because they only run periodically, and they often cannot get the information they need from the search engine providers, information like how many clicks an advertiser is likely to receive at different ranks.
If the advertiser is on a fixed budget, then he must also continually monitor his spending to make sure he is on target to meet his budget. Suppose, for example, that the advertiser has $1,000 to spend over the next month. If he sets his bid to $1.00, and 50 users click on his listing the first day, then he must lower his bid because his spending rate is too high. At $50/day, he will burn through his entire budget before the end of the month. Conversely, if only 10 users click on his listing the first day, then he must raise his bid because his spending rate is too low. The interaction between bids and budgets is complicated, and difficult to get right without constant adjustment. It can also lead to poor search results, since instead of seeing the best, most relevant advertisers, a user often simply sees the advertisers that are currently under their budgets.
All of these problems become even more complicated when an advertiser bids on multiple search terms. Each term receives a different number of searches and clicks, and requires different bids. The advertiser must somehow allocate his money among them in a way that optimizes his total profit. There are currently no good tools to help the advertiser do this, and even the best bidding agents make no attempt to raise and lower bids across multiple terms to match a fixed budget.
Many advertisers would prefer an alternative to this system. The elaborate bid structure requires too much micromanagement, and it obfuscates the only two issues that an advertiser really cares about: how much he has to pay, and what he gets in return. An advertiser would like the search engine provider to tell him that for $1,000 he can buy 1,000 clicks over the next month; or he can spend twice as much, and get twice as many clicks, or spend half as much, and get half as many clicks. This model is much more in line with other methods of advertising, like the Yellow Pages and Internet banner ads. When the information is distilled down to cost and clicks, it is clear that the entire concept of bids and ranks is unnecessary. An advertiser does not really care about what rank he appears at in a search result list. He cares about how many clicks he gets for his money, and how often those clicks turn into sales. Ideally, he can make his buying decisions on this information alone—how much he pays, and how many clicks he gets in return—and leave all the other details about where and when his listing shows up to the search engine provider. A system based on this idea gives the search engine provider complete freedom to decide which listings it should show in response to a user's query, so it solves the ferret problem in addition to being much simpler for advertisers.